Within 2 weeks, our 2-day crash course on Applied spatial modelling with R (April 13-14, 2016) will be given at the University of Leuven, Belgium: https://lstat.kuleuven.be/training/applied-spatial-modelling-with-r
You'll learn during this course the following elements:
- The sp package to handle spatial data (spatial points, lines, polygons, spatial data frames)
- Importing spatial data and setting the spatial projection
- Plotting spatial data on static and interactive maps
- Adding graphical components to spatial maps
- Manipulation of geospatial data, geocoding, distances, …
- Density estimation, kriging and spatial point pattern analysis
- Spatial regression
More information: https://lstat.kuleuven.be/training/applied-spatial-modelling-with-r. Registration can be done at https://lstat.kuleuven.be/forms/courses
With the release of RStudio add-in possibilities, a new area of productivity increase and expected new features for R users has arrived. Thanks to the help of Oliver who has written an RStudio add-in on top of taskscheduleR, scheduling and automating an R script from RStudio is now exactly one click away if you are working on Windows.
How? Just install these R packages and you have the add-in ready at the add-in tab in your RStudio session. Select your R script and schedule it to run any time you want. Hope this saves you some day-to-day time and feel free to help make additional improvements. More information: https://github.com/bnosac/taskscheduleR.
install.packages('data.table')
install.packages('knitr')
install.packages('miniUI')
install.packages('shiny')
install.packages("taskscheduleR", repos = "http://www.datatailor.be/rcube", type = "source")
If you are working on a Windows computer and want to schedule your R scripts while you are off running, sleeping or having a coffee break, the taskscheduleR package might be what you are looking for.
The taskscheduleR R package is available at https://github.com/bnosac/taskscheduleR and it allows R users to do the following:
i) Get the list of scheduled tasks
ii) Remove a task
iii) Add a task
- A task is basically a script with R code which is run through Rscript
- You can schedule tasks 'ONCE', 'MONTHLY', 'WEEKLY', 'DAILY', 'HOURLY', 'MINUTE', 'ONLOGON', 'ONIDLE'
- After the script has run, you can check the log which can be found at the same folder as the R script. It contains the stdout & stderr of the Rscript.
Below, you can find an example how you can schedule your R script once or daily in the morning.
library(taskscheduleR)
myscript <- system.file("extdata", "helloworld.R", package = "taskscheduleR")
## run script once within 62 seconds
taskscheduler_create(taskname = "myfancyscript", rscript = myscript,
schedule = "ONCE", starttime = format(Sys.time() + 62, "%H:%M"))
## run script every day at 09:10
taskscheduler_create(taskname = "myfancyscriptdaily", rscript = myscript,
schedule = "DAILY", starttime = "09:10")
## delete the tasks
taskscheduler_delete(taskname = "myfancyscript")
taskscheduler_delete(taskname = "myfancyscriptdaily")
- When the task has run, you can look at the log which contains everything from stdout and stderr. The log file is located at the directory where the R script is located.
## log file is at the place where the helloworld.R script was located
system.file("extdata", "helloworld.log", package = "taskscheduleR")
Who wants to set up an RStudio add-in for this?
Tomorrow, the next RBelgium meeting will be held at the bnosac offices. This is the schedule.
Interested? Feel free to join the event. More info: http://www.meetup.com/RBelgium/events/228427510/
• 18h00-18h30: enter & meet other R users
• 18h30-19h00: Web scraping with R: live scraping products & prices of www.delhaize.be
• 19h15-20h00: State-of-the-art classification algorithms with unbalanced data. Package unbalanced: Racing for Unbalanced Methods Selection.
